Some existing positioning services provide position information to requesting computing devices based on crowd-sourced data in such systems, the requesting computing devices provide a set of observed beacons and the positioning service returns an inferred approximate position of the requesting computing devices based on the set of observed beacons. The accuracy of the approximate position determined by the positioning service, however, is dependent on the quality of the crowd-sourced data, the modeling algorithms that estimate beacon models (e.g., that model data beacon data structures), and/or the position inference algorithms that calculate the approximate position of the requesting computing device. The crowd-sourced data may be noisy and unreliable due to differences in the devices providing the crowd-sourced data, the locations of the devices, and conditions under which the crowd-sourced data was obtained by the devices (e.g., signal strength, environment type, etc.). Further, one modeling algorithm or position inference algorithm may perform better than another algorithm on a particular set of crowd-sourced data, or in a particular geographic area. Existing systems fail to provide or enable a systematic analysis of crowd-sourced data quality and of performance of the modeling algorithms and the position inference algorithms.